The role of heterogenous environmental conditions in shaping the spatiotemporal distribution of competing Aedes mosquitoes in Panama: implications for the landscape of arboviral disease transmission

An optical system to detect, surveil, and kill flying insect vectors of human and crop pathogens

Sustainable and effective means to control flying insect vectors are critically needed, especially with widespread insecticide resistance and global climate change. Understanding and controlling vectors requires accurate information about their movement and activity, which is often lacking. The Photonic Fence (PF) is an optical system that uses machine vision, infrared light, and lasers to identify, track, and interdict vectors in flight. The PF examines an insect's outline, flight speed, and other flight parameters and if these match those of a targeted vector species, then a low-power, retina-safe laser kills it. We report on proof-of-concept tests of a large, field-sized PF (30 mL × 3 mH) conducted with Aedes aegypti, a mosquito that transmits dangerous arboviruses, and Diaphorina citri, a psyllid which transmits the fatal huanglongbing disease of citrus. In tests with the laser engaged, < 1% and 3% of A. aegypti and D. citri, respectfully, were recovered versus a 38% and 19% recovery when the lacer was silenced. The PF tracked, but did not intercept the orchid bee, Euglossa dilemma. The system effectively intercepted flying vectors, but not bees, at a distance of 30 m, heralding the use of photonic energy, rather than chemicals, to control flying vectors.

A Bayesian spatiotemporal approach to modelling arboviral diseases in Mexico

BACKGROUND

The objective of this study was to evaluate the spatial and temporal patterns of disease prevalence clusters of dengue (DENV), chikungunya (CHIKV) and Zika (ZIKV) virus and how socio-economic and climatic variables simultaneously influence the risk and rate of occurrence of infection in Mexico.

METHODS

To determine the spatiotemporal clustering and the effect of climatic and socio-economic covariates on the rate of occurrence of disease and risk in Mexico, we applied correlation methods, seasonal and trend decomposition using locally estimated scatterplot smoothing, hotspot analysis and conditional autoregressive Bayesian models.

RESULTS

We found cases of the disease are decreasing and a significant association between DENV, CHIKV and ZIKV cases and climatic and socio-economic variables. An increment of cases was identified in the northeastern, central west and southeastern regions of Mexico. Climatic and socio-economic covariates were significantly associated with the rate of occurrence and risk of the three arboviral disease cases.

CONCLUSION

The association of climatic and socio-economic factors is predominant in the northeastern, central west and southeastern regions of Mexico. DENV, CHIKV and ZIKV cases showed an increased risk in several states in these regions and need urgent attention to allocate public health resources to the most vulnerable regions in Mexico.

Using compartmental models and Particle Swarm Optimization to assess Dengue basic reproduction number R 0 for the Republic of Panama in the 1999-2022 period

Nowadays, the ability to make data-driven decisions in public health is of utmost importance. To achieve this, it is necessary for modelers to comprehend the impact of models on the future state of healthcare systems. Compartmental models are a valuable tool for making informed epidemiological decisions, and the proper parameterization of these models is crucial for analyzing epidemiological events. This work evaluated the use of compartmental models in conjunction with Particle Swarm Optimization (PSO) to determine optimal solutions and understand the dynamics of Dengue epidemics. The focus was on calculating and evaluating the rate of case reproduction, R 0 , for the Republic of Panama. Three compartmental models were compared: Susceptible-Infected-Recovered (SIR), Susceptible-Exposed-Infected-Recovered (SEIR), and Susceptible-Infected-Recovered Human-Susceptible-Infected Vector (SIR Human-SI Vector, SIR-SI). The models were informed by demographic data and Dengue incidence in the Republic of Panama between 1999 and 2022, and the susceptible population was analyzed. The SIR, SEIR, and SIR-SI models successfully provided R 0 estimates ranging from 1.09 to 1.74. This study provides, to the best of our understanding, the first calculation of R 0 for Dengue outbreaks in the Republic of Panama.

Satellite observation to assess dengue risk due to Aedes aegypti and Aedes albopictus in a subtropical city of Argentina

Earth observation environmental features measured through remote sensing and models of vector mosquitoes species Aedes aegypti and Ae. albopictus provide an advancement with regards to dengue risk in urban environments of subtropical areas of Argentina. The authors aim to estimate the effect of landscape coverage and spectral indices (Normalized Difference Vegetation Index [NDVI], Normalized Difference Water Index [NDWI] and Normalized Difference Built-up Index [NDBI]) on the larvae abundance of Ae. aegypti and Ae. albopictus in Eldorado, Misiones, Argentina using remote satellite sensors. Larvae of these species were collected monthly (June 2016 to April 2018), in four environments: tire repair shops, cemeteries, dwellings and an urban natural park. The proportion of landscape coverage (water, urban areas, bare soil, low vegetation and high vegetation) was determined from the supervised classification of Sentinel-2 images and spectral indices, calculated. The authors developed spatial models of both vector species by generalized linear mixed models. The model's results showed that Ae. aegypti larvae abundance was better modelled by NDVI minimum values, NDBI maximum values and the interaction between them. For Ae. albopictus proportion of bare soil, low vegetation and the interaction between both variables explained better the abundance.

Invasion Pattern of Aedes aegypti in the Native Range of Ae. albopictus in Vietnam Revealed by Biogeographic and Population Genetic Analysis

Since its introduction to Asia, Aedes aegypti has coexisted with the native species Ae. albopictus and has been reported to transmit several infectious diseases. However, the development of efficient disease prevention and vector control is hindered by the relatively poor understanding of the biogeography and the genetic diversity of Ae. aegypti in the region. This study aimed to determine the invasion patterns of Ae. aegypti by evaluating the distribution and abundance of Ae. aegypti and Ae. albopictus in different climatic regions (northern temperate and southern tropical regions) and habitats (domestic, peri-domestic, and natural). We further analyzed the genetic diversity and phylogenetic relationships of Ae. aegypti populations in Vietnam using mitochondrial COI gene sequences. Both Aedes species were observed at most of the study sites, but only Ae. albopictus thrived in northern mountainous areas. In sympatric ranges, the individual abundance of the species was influenced by regional climate and habitats. The tropical climate and availability of domestic containers facilitated the dominance of Ae. aegypti, whereas temperate climates and natural breeding sites facilitated that of Ae. albopictus. In addition, many genetic polymorphisms were detected in the Ae. aegypti populations, which formed two distinct genetic groups; however, this genetic diversity is unlikely to be relevant to the invasive success of Ae. aegypti. These findings provide insights into the mechanisms and patterns of Ae. Aegypti invasion, which depend on the climate and reproductive strategies in the native range of Ae. albopictus in Asia.

Metagenomic Analysis of Togaviridae in Mosquito Viromes Isolated From Yunnan Province in China Reveals Genes from Chikungunya and Ross River Viruses

We collected 5,500 mosquitoes belonging to six species in three locations in China. Their viromes were tested using metagenomic sequencing and bioinformatic analysis. The affluent viral sequences that were detected and annotated belong to 22 viral taxonomic families. Then, PCR was performed to confirm the results, followed by phylogenetic analysis. Herein, part of mosquito virome was identified, including chikungunya virus (CHIKV), Getah virus (GETV), and Ross river virus (RRV). After metagenomic analysis, seven CHIKV sequences were verified by PCR amplification, among which CHIKV-China/YN2018-1 had the highest homology with the CHIKV isolated in Senegal, 1983, with a nucleotide (nt) identity of at least 81%, belonging to genotype West Africa viral genes. Five GETV sequences were identified, which had a high homology with the GETV sequences isolated from Equus caballus in Japan, 1978, with a (nt) identity of at least 97%. The newly isolated virus CHIKV-China/YN2018-1 became more infectious after passage of the BHK-21 cell line to the Vero cell line. The newly identified RRV gene had the highest homology with the 2006 RRV isolate from Australia, with a (nt) identity of at least 94%. In addition, numerous known and unknown viruses have also been detected in mosquitoes from Yunnan province, China, and propagation tests will be carried out.

Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City

The present analysis uses the data of confirmed incidence of dengue cases in the metropolitan region of Panama from 1999 to 2017 and climatic variables (air temperature, precipitation, and relative humidity) during the same period to determine if there exists a correlation between these variables. In addition, we compare the predictive performance of two regression models (SARIMA, SARIMAX) and a recurrent neural network model (RNN-LSTM) on the dengue incidence series. For this data from 1999–2014 was used for training and the three subsequent years of incidence 2015–2017 were used for prediction. The results show a correlation coefficient between the climatic variables and the incidence of dengue were low but statistical significant. The RMSE and MAPE obtained for the SARIMAX and RNN-LSTM models were 25.76, 108.44 and 26.16, 59.68, which suggest that any of these models can be used to predict new outbreaks. Although, it can be said that there is a limited role of climatic variables in the outputs the models. The value of this work is that it helps understand the behaviour of cases in a tropical setting as is the Metropolitan Region of Panama City, and provides the basis needed for a much needed early alert system for the region.